The present disclosure relates in general to systems and methodologies for developing e-commerce business strategies. More specifically, the present disclosure relates to systems and methodologies for developing targeted marketing-type and advertising-type e-commerce business strategies based on the identification and grouping of predictive cognitive traits from among a population.
The ability to target advertisements, in terms of both content and scope, to specific population segments is a fundamental requirement for effective marketing and advertising campaigns. Marketing and advertising business strategies often involve an analysis of a population's tastes and needs based on information that members of the population share through various electronic media. In e-commerce settings, for example, the analysis employed is often semantic, wherein what a user searches or writes about is used to infer what a user needs. An example of a semantic-based advertising strategy is known generally as semantic targeting. Semantic targeting is a technique enabling the delivery of targeted advertising for advertisements appearing on websites and is used by online publishers and advertisers to increase the effectiveness of their campaigns. The selection of advertisements is served by automated systems based on the content displayed to the user.
Semantic-based marketing and advertising strategies typically involve scanning the content of web-pages to identify keywords. However, such systems are unable to identify the context of the entire page and therefore the inferences drawn from such analysis is inherently imprecise. For example, the word “orange” can be a color, a fruit, a telecommunications company, a mountain bike, and countless other variants.
Social network information is also used to analyze a population's tastes and needs. In a typical configuration, social network information is aggregated and associated statically with users and their social networks. Social network information is limited in that the extracted data is focused on users who have established connections to one another through the social network, and users outside of that social network are not typically included.
Neither sematic-based marketing and advertising strategies nor social network information analysis techniques create categories of potential e-commerce marketing or advertising population segments rapidly and dynamically, and independently from the limitations of traditional semantic-based or social network-based analyses.